Introducing the @sequoia Gen AI Market Map!🌎 We’ve decided to map out this emerging frontier, thanks to all the contributions and feedback we’ve received.
This space is moving quickly – this map is a living document, so keep the suggestions coming! Who else should we include?
Here are some of the projects that get us excited:
Marketers can’t have all the fun – the rest of us get writer’s block too. What about a new text editor with generation at its core? Think Google Docs that can generate entire paragraphs for you. @Get_Writer#Moonbeam#Lex are exploring this space. writer.com
Now that machines can read, they can auto populate companies' knowledge bases with semantic understanding. This means Gen AI can answer questions and file and retrieve documents - a smart, self organizing workspace. @gleanwork@memdotaiglean.com
Coding is the highest leverage form of language, so generative coding may just be the holy grail of Gen AI. The early results from @github and @Replit suggest a future where anybody with product sensibility can generate a fully functioning app. replit.com
Text-to-image models make us feel like the next Picasso or Studio Ghibli. @OpenAI@midjourney@StabilityAI@WOMBOAI1@craiyonAI each allow us to create images with a different mood or timbre. Up next: more sophisticated editors, social experiences, search engines, and more.
It’s exciting to think about how AI can help rapidly concept fantastic new worlds in film, television, and more. @SALT_VERSE is creating a 70s style scifi multi-plot film, built entirely with generative models.
Gaming experiences may be generated by users or machines on the fly – with AAA level quality. @AiDungeon was one of the first projects to imagine the experience using text-based models, and image and 3D should be ready soon. Here is “Raccoon Heist” from @simonwu:
Design - from interiors/architecture to software to physical products - is up for reinvention. Moodboarding, inspiration, iteration should become drastically faster and better. Less time wasted pushing pixels or organizing artifacts manually. @diagram is a nice example here.
Video - one of the richest but hardest-to-master mediums of communication - is getting democratized. @runwayml is making it possible for anybody to create Hollywood quality movies with text-to-video, text-based colorization and more. runwayml.com
Speech Gen allows us to immerse ourselves in experiences that bring out the emotional and expressive qualities of human speech. @play_htpodcast.ai is bringing some of these legends to life in podcast form. We're deep in the uncanny valley with this one.
These apps provide an interesting glimpse into what the future may hold. Once you see a machine produce complex functioning code or brilliant images, it’s hard to imagine a future where machines don’t play a fundamental role in how we work and create.
Update: grateful & overwhelmed by the sheer volume of additions, corrections, and support on this map! Getting to work organizing this all into an updated map. Will be back soon. Keep the suggestions coming :)
As promised: here is our @sequoia Gen AI market map V2! Very thankful for the hundreds of folks who wrote in additions and edits. AI people are amazing 🥹
We've tried to keep this map high signal:noise and only included apps where generative AI (defined as LLMs or other unsupervised learning + generative) is core to the product experience. We also couldn't fit every single image or copy generation app.
This map is still far from complete & far from perfect. We had the difficult task of filtering through a long list of amazing companies and narrowing it down to this set. I'm sure I made mistakes along the way. If there's something I overlooked, please DM me!
Again, this is a living document, so please keep sending tips and edits over :)
Last week we hosted our 3rd annual @sequoia AI Ascent, bringing together 150+ of the top founders and researchers in AI.
Our star-studded speakers dropped hot takes and sparked inspiration for a standing-room-only audience:
- jensen huang on token-generating AI factories as the new industrial infrastructure
- @sama on the timeline to agents/science/robots and ChatGPT as Operating System
- @JeffDean on how he's been trying vibes coding and his prediction on the timeline to an AI junior engineer (sooner than you might think!)
- @btaylor on becoming Facebook CTO at age 29, pricing/packaging agents, and advice for application layer founders
- @DrJimFan of NVIDIA on why simulation is the key to robots passing the physical Turing test
- @ChaseLochmiller of Crusoe on the moving industrial bottlenecks to scaling AI: power, steel, real estate
- @mikeyk of Anthropic on MCP, Claude Code, and on his framework for product-building when the models themselves are changing so quickly
- @hwchase17 of Langchain on ambient agents and his vision for an "agent inbox"
- @danintheory of OpenAI on how RL is going from the "cherry on top" of the pre-training cake to the main course, and what questions einstein-v1907-super-high might be able to answer by scaling reasoning compute
- @gradypb on the new physics of distribution, the Leone merchandising cycle applied to AI foundation models vs application startups, and his advice to startups to "run like heck"
- @Konstantine on the agent economy and stochastic mindset
And many more.
Thanks to our community for making this the best, most buzzy, content-rich AI Ascent yet 💚 Detailed session recaps to follow -- and replay videos dropping over the course of the week.
Jensen Huang of @nvidia:
* AI is the industry of the future. We're manufacturing tokens, not sneakers. AI Factories are the new industrial infrastructure.
* We’re moving from "servers retrieving data" to "AI factories generating tokens" as the new economic engine.
* Preserving and nurturing the open source ecosystem is key to pressing America's competitive advantage and innovation cycle in AI.
* AI Security will become an increasingly hot topic as agents take off
@ChaseLochmiller of @CrusoeAI:
* AI data centers have become industrial-scale “AI factories.” Managing 100K+ GPU clusters requires extreme power and infrastructure capabilities—far beyond traditional data centers.
* The bottleneck has shifted from chips to energy, steel, and physical buildout. Power availability is now the core constraint; Crusoe even manufactures its own switchgear and explores building power plants.
* Geographic and sovereign AI demands are driving data center dispersion. Power availability is creating new AI hubs; nation-state data sovereignty adds to the mix.
* AI growth is accelerating the demand for new energy sources, including nuclear.